{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocess Google Streetview House Number Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T22:36:20.043987Z",
     "start_time": "2020-06-21T22:36:20.041643Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T22:36:21.724417Z",
     "start_time": "2020-06-21T22:36:20.045555Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import os\n",
    "import sys\n",
    "import tarfile\n",
    "import tensorflow as tf\n",
    "from IPython.display import display, Image\n",
    "from scipy import ndimage\n",
    "import h5py\n",
    "from PIL import Image\n",
    "import PIL.Image as Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T22:36:21.727784Z",
     "start_time": "2020-06-21T22:36:21.725745Z"
    }
   },
   "outputs": [],
   "source": [
    "DATA_PATH = Path('images', 'svhn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T22:36:21.736340Z",
     "start_time": "2020-06-21T22:36:21.728937Z"
    }
   },
   "outputs": [],
   "source": [
    "results_path = Path('results', 'svhn')\n",
    "if not results_path.exists():\n",
    "    results_path.mkdir(parents=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the source data (Format 1) from [Stanford Deep Learning](http://ufldl.stanford.edu/housenumbers/) and place the extracted `train` and `test` folders in the directory `images/svhn`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse Bounding Box information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adapted from [this](https://github.com/Bartzi/see/blob/master/datasets/svhn/svhn_dataextract_tojson.py) script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T22:36:21.748738Z",
     "start_time": "2020-06-21T22:36:21.737579Z"
    }
   },
   "outputs": [],
   "source": [
    "class DigitStructFile:\n",
    "    def __init__(self, inf):\n",
    "        self.inf = h5py.File(inf, 'r')\n",
    "        self.digitStructName = self.inf['digitStruct']['name']\n",
    "        self.digitStructBbox = self.inf['digitStruct']['bbox']\n",
    "\n",
    "    def getName(self, n):\n",
    "        return ''.join([chr(c[0]) for c in self.inf[self.digitStructName[n][0]].value])\n",
    "\n",
    "    def bboxHelper(self, attr):\n",
    "        if (len(attr) > 1):\n",
    "            attr = [self.inf[attr.value[j].item()].value[0][0]\n",
    "                    for j in range(len(attr))]\n",
    "        else:\n",
    "            attr = [attr.value[0][0]]\n",
    "        return attr\n",
    "\n",
    "    def getBbox(self, n):\n",
    "        bbox = {}\n",
    "        bb = self.digitStructBbox[n].item()\n",
    "        bbox['height'] = self.bboxHelper(self.inf[bb][\"height\"])\n",
    "        bbox['label'] = self.bboxHelper(self.inf[bb][\"label\"])\n",
    "        bbox['left'] = self.bboxHelper(self.inf[bb][\"left\"])\n",
    "        bbox['top'] = self.bboxHelper(self.inf[bb][\"top\"])\n",
    "        bbox['width'] = self.bboxHelper(self.inf[bb][\"width\"])\n",
    "        return bbox\n",
    "\n",
    "    def getDigitStructure(self, n):\n",
    "        s = self.getBbox(n)\n",
    "        s['name'] = self.getName(n)\n",
    "        return s\n",
    "\n",
    "    def getAllDigitStructure(self):\n",
    "        return [self.getDigitStructure(i) for i in range(len(self.digitStructName))]\n",
    "\n",
    "    def getAllDigitStructure_ByDigit(self):\n",
    "        pictDat = self.getAllDigitStructure()\n",
    "        result = []\n",
    "        structCnt = 1\n",
    "        for i in range(len(pictDat)):\n",
    "            item = {'filename': pictDat[i][\"name\"]}\n",
    "            figures = []\n",
    "            for j in range(len(pictDat[i]['height'])):\n",
    "                figure = {}\n",
    "                figure['height'] = pictDat[i]['height'][j]\n",
    "                figure['label'] = pictDat[i]['label'][j]\n",
    "                figure['left'] = pictDat[i]['left'][j]\n",
    "                figure['top'] = pictDat[i]['top'][j]\n",
    "                figure['width'] = pictDat[i]['width'][j]\n",
    "                figures.append(figure)\n",
    "            structCnt = structCnt + 1\n",
    "            item['boxes'] = figures\n",
    "            result.append(item)\n",
    "        return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T22:36:21.761348Z",
     "start_time": "2020-06-21T22:36:21.749916Z"
    }
   },
   "outputs": [],
   "source": [
    "def generate_dataset(data, path):\n",
    "    n = len(data)\n",
    "    dataset = np.ndarray([n, 32, 32, 3], dtype='float32')\n",
    "    labels = np.ones([n, 6], dtype=int) * 10\n",
    "    for i in range(n):\n",
    "        if i % 5000 == 0:\n",
    "            print(i, end=' ', flush=True)\n",
    "        im = Image.open(path / data[i]['filename'])\n",
    "        boxes = data[i]['boxes']\n",
    "        num_digit = len(boxes)\n",
    "        labels[i, 0] = num_digit\n",
    "        top = np.ndarray([num_digit], dtype='float32')\n",
    "        left = np.ndarray([num_digit], dtype='float32')\n",
    "        height = np.ndarray([num_digit], dtype='float32')\n",
    "        width = np.ndarray([num_digit], dtype='float32')\n",
    "        for j in np.arange(num_digit):\n",
    "            if j < 5:\n",
    "                labels[i, j + 1] = boxes[j]['label']\n",
    "                if boxes[j]['label'] == 10:\n",
    "                    labels[i, j + 1] = 0\n",
    "            else:\n",
    "                print('\\n#', i, 'image has more than 5 digits.')\n",
    "            top[j] = boxes[j]['top']\n",
    "            left[j] = boxes[j]['left']\n",
    "            height[j] = boxes[j]['height']\n",
    "            width[j] = boxes[j]['width']\n",
    "\n",
    "        im_top = np.amin(top)\n",
    "        im_left = np.amin(left)\n",
    "        im_height = np.amax(top) + height[np.argmax(top)] - im_top\n",
    "        im_width = np.amax(left) + width[np.argmax(left)] - im_left\n",
    "\n",
    "        im_top = np.floor(im_top - 0.1 * im_height)\n",
    "        im_left = np.floor(im_left - 0.1 * im_width)\n",
    "        im_bottom = np.amin([np.ceil(im_top + 1.2 * im_height), im.size[1]])\n",
    "        im_right = np.amin([np.ceil(im_left + 1.2 * im_width), im.size[0]])\n",
    "\n",
    "        im = im.crop((im_left, im_top, im_right, im_bottom))\n",
    "        im = np.array(im.resize([32, 32], Image.ANTIALIAS), dtype='float32')\n",
    "        im = (im - 128) / 128\n",
    "        dataset[i, :, :, :] = im[:, :, :]\n",
    "    return dataset, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T22:41:07.511383Z",
     "start_time": "2020-06-21T22:36:21.762979Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " train\n",
      "0 5000 10000 15000 20000 25000 \n",
      "# 29929 image has more than 5 digits.\n",
      "30000 \n",
      " test\n",
      "0 5000 10000 "
     ]
    }
   ],
   "source": [
    "for folder in ['train', 'test']:\n",
    "    print('\\n', folder)\n",
    "    target = DATA_PATH / folder / 'digitStruct.mat'\n",
    "    dsf = DigitStructFile(target)\n",
    "    data = dsf.getAllDigitStructure_ByDigit()\n",
    "    dataset, labels = generate_dataset(data, DATA_PATH / folder)\n",
    "    np.save(DATA_PATH / f'X_{folder}', dataset)\n",
    "    np.save(DATA_PATH / f'y_{folder}', labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot sample images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T22:41:07.596653Z",
     "start_time": "2020-06-21T22:41:07.512592Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train = np.load(DATA_PATH / 'X_train.npy')\n",
    "y_train = np.load(DATA_PATH / 'y_train.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T22:43:27.786190Z",
     "start_time": "2020-06-21T22:43:26.694322Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=8, figsize=(20, 5))\n",
    "axes = axes.flatten()\n",
    "for i, ax in enumerate(axes):\n",
    "    ax.imshow(np.squeeze((X_train[i] * 128) + 128).astype(int))\n",
    "    ax.axis('off')\n",
    "    ax.set_title(''.join([str(d) for d in y_train[i][1:] if d < 10]))\n",
    "fig.tight_layout()\n",
    "fig.savefig(results_path / 'sample_img', dpi=300);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:ml4t-dl]",
   "language": "python",
   "name": "conda-env-ml4t-dl-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
